{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1_CatcdIwkjO"
      },
      "source": [
        "### Installation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "MvLJWmA_wkjO"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "\n",
        "!pip install --no-deps bitsandbytes accelerate xformers==0.0.29 peft trl triton\n",
        "!pip install --no-deps cut_cross_entropy unsloth_zoo\n",
        "!pip install sentencepiece protobuf datasets huggingface_hub hf_transfer\n",
        "!pip install --no-deps unsloth"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9lh7U8BEwkjO"
      },
      "source": [
        "### Loading model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313,
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          ]
        },
        "id": "QmUBVEnvCDJv",
        "outputId": "39bec330-9f29-4e7c-ba0e-586bb4084549"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
            "🦥 Unsloth Zoo will now patch everything to make training faster!\n",
            "==((====))==  Unsloth 2025.1.8: Fast Llama patching. Transformers: 4.47.1.\n",
            "   \\\\   /|    GPU: NVIDIA A100-SXM4-40GB. Max memory: 39.557 GB. Platform: Linux.\n",
            "O^O/ \\_/ \\    Torch: 2.5.1+cu124. CUDA: 8.0. CUDA Toolkit: 12.4. Triton: 3.1.0\n",
            "\\        /    Bfloat16 = TRUE. FA [Xformers = 0.0.29. FA2 = False]\n",
            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n",
            "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "54caa1dffce942dea371b047bb6d1553",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/5.96G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "89e895a711fb46ea8b5e4ce643528fef",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/236 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4d913a8d336045ad9621f2ca43db7d93",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/52.9k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3b717f91365f4abda9efa667166fb0cb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/483 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5cb39938d947406db8bd0c12e29eec95",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/17.2M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from unsloth import FastLanguageModel\n",
        "import torch\n",
        "max_seq_length = 2048\n",
        "dtype = None\n",
        "load_in_4bit = True\n",
        "\n",
        "model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "    model_name = \"unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit\",\n",
        "    max_seq_length = max_seq_length,\n",
        "    dtype = dtype,\n",
        "    load_in_4bit = load_in_4bit,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SXd9bTZd1aaL"
      },
      "source": [
        "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6bZsfBuZDeCL",
        "outputId": "0d6debbf-a035-45e8-a29f-f60b12ba4238"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Unsloth 2025.1.8 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n"
          ]
        }
      ],
      "source": [
        "model = FastLanguageModel.get_peft_model(\n",
        "    model,\n",
        "    r = 16,\n",
        "    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
        "                      \"gate_proj\", \"up_proj\", \"down_proj\",],\n",
        "    lora_alpha = 16,\n",
        "    lora_dropout = 0,\n",
        "    bias = \"none\",\n",
        "    use_gradient_checkpointing = \"unsloth\",\n",
        "    random_state = 3407,\n",
        "    use_rslora = False,\n",
        "    loftq_config = None,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 83,
          "referenced_widgets": [
            "cc498fe2882b48d1be3e5672d0f594dd",
            "c659be9dbf9a4d07a12b44e1e530adce",
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        },
        "id": "fz85BY650m5s",
        "outputId": "92e20917-8db5-4e3f-c54d-e4ffaa82aaa1"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Unsloth: Will map <|im_end|> to EOS = <|im_end|>.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "cc498fe2882b48d1be3e5672d0f594dd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/26952 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of finance-related conversations: 26952\n"
          ]
        }
      ],
      "source": [
        "from unsloth.chat_templates import get_chat_template\n",
        "from datasets import load_dataset\n",
        "\n",
        "\n",
        "tokenizer = get_chat_template(\n",
        "    tokenizer,\n",
        "    chat_template=\"chatml\",\n",
        "    mapping={\"role\": \"from\", \"content\": \"value\", \"user\": \"human\", \"assistant\": \"gpt\"}\n",
        ")\n",
        "\n",
        "def apply_template(examples):\n",
        "    messages = examples[\"conversations\"]\n",
        "    return {\n",
        "        \"text\": [\n",
        "            tokenizer.apply_chat_template(message, tokenize=False, add_generation_prompt=False)\n",
        "            for message in messages\n",
        "        ]\n",
        "    }\n",
        "\n",
        "dataset = load_dataset(\"heladell/Finance_DeepSeek-R1-Distill-dataset\", split = \"train\")\n",
        "\n",
        "dataset = dataset.map(\n",
        "    apply_template,\n",
        "    batched=True,\n",
        "    batch_size=100,\n",
        "    remove_columns=dataset.column_names\n",
        ")\n",
        "\n",
        "print(f\"Number of finance-related conversations: {len(dataset)}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "idAEIeSQ3xdS"
      },
      "source": [
        "### Train the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "5cfc070b4ee04e4597e4409bcad1723c",
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            "f2eb1e1cf5ea444aa5002aceb265f787"
          ]
        },
        "id": "95_Nn-89DhsL",
        "outputId": "b579622e-4b9d-4735-f684-5f81aad63ee9"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5cfc070b4ee04e4597e4409bcad1723c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map (num_proc=2):   0%|          | 0/26952 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from trl import SFTTrainer\n",
        "from transformers import TrainingArguments\n",
        "from unsloth import is_bfloat16_supported\n",
        "\n",
        "trainer = SFTTrainer(\n",
        "    model = model,\n",
        "    tokenizer = tokenizer,\n",
        "    train_dataset = dataset,\n",
        "    dataset_text_field = \"text\",\n",
        "    max_seq_length = max_seq_length,\n",
        "    dataset_num_proc = 2,\n",
        "    packing = False,\n",
        "    args = TrainingArguments(\n",
        "        per_device_train_batch_size = 2,\n",
        "        gradient_accumulation_steps = 4,\n",
        "        warmup_steps = 5,\n",
        "        max_steps = 60,\n",
        "        # num_train_epochs = 1,\n",
        "        learning_rate = 2e-4,\n",
        "        fp16 = not is_bfloat16_supported(),\n",
        "        bf16 = is_bfloat16_supported(),\n",
        "        logging_steps = 1,\n",
        "        optim = \"adamw_8bit\",\n",
        "        weight_decay = 0.01,\n",
        "        lr_scheduler_type = \"linear\",\n",
        "        seed = 3407,\n",
        "        output_dir = \"outputs\",\n",
        "        report_to = \"none\",\n",
        "    ),\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "yqxqAZ7KJ4oL",
        "outputId": "be7b46be-db3a-4f1c-82a6-20898283d24c"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1\n",
            "   \\\\   /|    Num examples = 26,952 | Num Epochs = 1\n",
            "O^O/ \\_/ \\    Batch size per device = 2 | Gradient Accumulation steps = 4\n",
            "\\        /    Total batch size = 8 | Total steps = 60\n",
            " \"-____-\"     Number of trainable parameters = 41,943,040\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='60' max='60' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [60/60 04:43, Epoch 0/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.716200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.684400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.534900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.695000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.687300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.583500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.602100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.423500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.542600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.634800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.615100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.560900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.521300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.509600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.487600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>16</td>\n",
              "      <td>0.523600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>17</td>\n",
              "      <td>0.830200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>18</td>\n",
              "      <td>0.502800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>19</td>\n",
              "      <td>0.643900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>20</td>\n",
              "      <td>0.706300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>21</td>\n",
              "      <td>0.555400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>22</td>\n",
              "      <td>0.483200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>23</td>\n",
              "      <td>0.506300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>24</td>\n",
              "      <td>0.580700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>0.440700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>26</td>\n",
              "      <td>0.536100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>27</td>\n",
              "      <td>0.714800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>28</td>\n",
              "      <td>0.381400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>29</td>\n",
              "      <td>0.437800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>30</td>\n",
              "      <td>0.643300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>31</td>\n",
              "      <td>0.680200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>32</td>\n",
              "      <td>0.632800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>33</td>\n",
              "      <td>0.535200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>34</td>\n",
              "      <td>0.606800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>35</td>\n",
              "      <td>0.689500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>36</td>\n",
              "      <td>0.536100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>37</td>\n",
              "      <td>0.395500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>38</td>\n",
              "      <td>0.604800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>39</td>\n",
              "      <td>0.600700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>40</td>\n",
              "      <td>0.385800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>41</td>\n",
              "      <td>0.567500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>42</td>\n",
              "      <td>0.473400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>43</td>\n",
              "      <td>0.432700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>44</td>\n",
              "      <td>0.532900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>45</td>\n",
              "      <td>0.583600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>46</td>\n",
              "      <td>0.515300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>47</td>\n",
              "      <td>0.567500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>48</td>\n",
              "      <td>0.546000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>49</td>\n",
              "      <td>0.514700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>0.561100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>51</td>\n",
              "      <td>0.568300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>52</td>\n",
              "      <td>0.523900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>53</td>\n",
              "      <td>0.454500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>54</td>\n",
              "      <td>0.512500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>55</td>\n",
              "      <td>0.522800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>56</td>\n",
              "      <td>0.443200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>57</td>\n",
              "      <td>0.555300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>58</td>\n",
              "      <td>0.413200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>59</td>\n",
              "      <td>0.495100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>60</td>\n",
              "      <td>0.567100</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "trainer_stats = trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ekOmTR1hSNcr"
      },
      "source": [
        "<a name=\"Inference\"></a>\n",
        "### Inference\n",
        "Let's run the model! You can change the instruction and input - leave the output blank!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kR3gIAX-SM2q",
        "outputId": "8d51d123-060a-4db4-d686-df5ad9ebec6d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<|im_start|>user\n",
            "how can you use technical analysis like MACD and RSI to predict the stock value<|im_end|>\n",
            "<|im_start|>assistant\n",
            "<think>\n",
            "Okay, so I'm trying to figure out how to use technical analysis tools like MACD and RSI to predict stock prices. I'm pretty new to investing, so I need to break this down step by step. \n",
            "\n",
            "First, I know that technical analysis is all about looking at charts and patterns to predict where the price might go. MACD and RSI are two different indicators, so I should understand each one separately.\n",
            "\n",
            "Starting with MACD, I remember it's a moving average convergence divergence indicator. I think it involves two moving averages. Maybe one is for the stock price and the other is for the volume? Or is it something else? Wait, I think it's the MACD line, which is the difference between two moving averages, and the signal line. The MACD line crosses above or below the signal line to indicate overbought or oversold conditions. \n",
            "\n",
            "Then there's RSI, which stands for Relative Strength Index. I believe RSI measures the strength of a stock's upward movement compared to its downward movement. It's usually between 0 and 100. If RSI is above 70, it's overbought, and below 30 is oversold. So, when RSI is above 70, the stock might be overvalued, and when it's below 30, it's undervalued.\n",
            "\n",
            "I'm not entirely sure how these indicators work together. Maybe they can be used to confirm each other? Like, if the MACD line is going above the signal line, and RSI is also above 70, that might be a strong signal that the stock is overbought and could go down.\n",
            "\n",
            "But how do I actually use these to predict stock values? I think I need to set up alerts or something when these conditions are met. But I'm not sure how to set that up with my trading platform. Do I need to code it, or can I set up predefined alerts? \n",
            "\n",
            "I also wonder about the timeframes. Should I use daily, weekly, or monthly charts? Probably depends on the stock's behavior. Maybe a shorter timeframe like daily is better for shorter-term trades, while weekly is for longer-term. But I'm not sure.\n",
            "\n",
            "Another thing is interpreting the signals. Sometimes the indicators can give false signals, especially if the market is volatile. So, I need to have some experience or maybe use some filters to avoid whipsawing. Maybe combining multiple indicators or using volume data could help confirm the signals.\n",
            "\n",
            "I'm also curious about how\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[\"<|im_start|>user\\nhow can you use technical analysis like MACD and RSI to predict the stock value<|im_end|>\\n<|im_start|>assistant\\n<think>\\nOkay, so I'm trying to figure out how to use technical analysis tools like MACD and RSI to predict stock prices. I'm pretty new to investing, so I need to break this down step by step. \\n\\nFirst, I know that technical analysis is all about looking at charts and patterns to predict where the price might go. MACD and RSI are two different indicators, so I should understand each one separately.\\n\\nStarting with MACD, I remember it's a moving average convergence divergence indicator. I think it involves two moving averages. Maybe one is for the stock price and the other is for the volume? Or is it something else? Wait, I think it's the MACD line, which is the difference between two moving averages, and the signal line. The MACD line crosses above or below the signal line to indicate overbought or oversold conditions. \\n\\nThen there's RSI, which stands for Relative Strength Index. I believe RSI measures the strength of a stock's upward movement compared to its downward movement. It's usually between 0 and 100. If RSI is above 70, it's overbought, and below 30 is oversold. So, when RSI is above 70, the stock might be overvalued, and when it's below 30, it's undervalued.\\n\\nI'm not entirely sure how these indicators work together. Maybe they can be used to confirm each other? Like, if the MACD line is going above the signal line, and RSI is also above 70, that might be a strong signal that the stock is overbought and could go down.\\n\\nBut how do I actually use these to predict stock values? I think I need to set up alerts or something when these conditions are met. But I'm not sure how to set that up with my trading platform. Do I need to code it, or can I set up predefined alerts? \\n\\nI also wonder about the timeframes. Should I use daily, weekly, or monthly charts? Probably depends on the stock's behavior. Maybe a shorter timeframe like daily is better for shorter-term trades, while weekly is for longer-term. But I'm not sure.\\n\\nAnother thing is interpreting the signals. Sometimes the indicators can give false signals, especially if the market is volatile. So, I need to have some experience or maybe use some filters to avoid whipsawing. Maybe combining multiple indicators or using volume data could help confirm the signals.\\n\\nI'm also curious about how\"]"
            ]
          },
          "execution_count": 38,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from transformers import TextStreamer\n",
        "\n",
        "# Load model for inference\n",
        "model = FastLanguageModel.for_inference(model)\n",
        "\n",
        "messages = [\n",
        "    {\"from\": \"human\", \"value\": \"how can you use technical analysis like MACD and RSI to predict the stock value\"},\n",
        "]\n",
        "inputs = tokenizer.apply_chat_template(\n",
        "    messages,\n",
        "    tokenize=True,\n",
        "    add_generation_prompt=True,\n",
        "    return_tensors=\"pt\",\n",
        ").to(\"cuda\")\n",
        "\n",
        "text_streamer = TextStreamer(tokenizer)\n",
        "outputs = model.generate(input_ids=inputs, streamer=text_streamer, max_new_tokens=512, use_cache=True)\n",
        "tokenizer.batch_decode(outputs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uMuVrWbjAzhc"
      },
      "source": [
        "<a name=\"Save\"></a>\n",
        "### Saving, loading finetuned models\n",
        "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n",
        "\n",
        "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 164,
          "referenced_widgets": [
            "1b99bab9a1c34f948f00f7b4b8b3331b",
            "e00b184ed1b94f1ab52d759d829f3517",
            "eafe2b2ab5bb445eaf7a4f007f368c2c",
            "4bf7988eebf9497b8a929e2165ce0b98",
            "356ddfea9df8444898bff9656ddfc018",
            "1ce8fc9e02d04473b71a455d755e8564",
            "efd1211d17ba458fb4fe0be5a1871b94",
            "a2bf7b6dc94244bba838fde11c8f6122",
            "dec0708d822f41ffbd3486c82b3bf40c",
            "8c440fd2b2504d21ade4ad1b269ac64f",
            "cb7e574208b442ae8d1e53d5aa81257e",
            "9af6103cb4a444c0ba3704b4bf5fc60a",
            "01f891b4d1a84bb0a6c0e1b7b5998adc",
            "3ae4a4308ef7449f9a8ec3603de30173",
            "5e920e6ab20b4fb9833f15c7d0e47402",
            "daa8058ca6f94b8d915c1f63bc189d79",
            "d09f5c760dca45a5bc97dad905212c8d",
            "9bbb5ae1a35441ec8525270973cacb67",
            "ceea6f70324845c7bf5236f1abbe8d8e",
            "6664ffe1695341ee916e10aaf992ad43",
            "83f13d2405e74df1a5ef46b7d0cb2cc0",
            "92973fc345784ab9b200b38d418c011c",
            "62911167b31f491b937de5cdc3abe0a1",
            "ce11946b0a9f4b34810658707466758a",
            "65f07c542667408ba35c9bc28c82c36e",
            "0643ff84dc584f22a700d6f551daa4bf",
            "28ddfe2f2eee4ba28ded54bdcf3ccd99",
            "cbd8f001ea6b4d0690cb9affb127b1f7",
            "6c2c1a2a21fc4760a4bfdbbbaded5286",
            "ae8bc83471bb432bbba4075908da76de",
            "22a5c0967e4f45ccba8760ad1712f4bc",
            "e56d0e56bc1b41268185031c296677af",
            "f528491838b34c00a20c667a80628683"
          ]
        },
        "id": "upcOlWe7A1vc",
        "outputId": "7d5ab190-9105-4495-b0a5-bf5d756a6931"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "1b99bab9a1c34f948f00f7b4b8b3331b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "README.md:   0%|          | 0.00/256 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9af6103cb4a444c0ba3704b4bf5fc60a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "62911167b31f491b937de5cdc3abe0a1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/168M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved model to https://huggingface.co/heladell/Finance_DeepSeek-R1-Distill-Llama-8B_QLoRA\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No files have been modified since last commit. Skipping to prevent empty commit.\n",
            "WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit.\n"
          ]
        }
      ],
      "source": [
        "model.save_pretrained(\"Finance_DeepSeek-R1-Distill-Llama-8B_QLoRA\")\n",
        "tokenizer.save_pretrained(\"Finance_DeepSeek-R1-Distill-Llama-8B_QLoRA\")\n",
        "\n",
        "# Push to huggingface\n",
        "model.push_to_hub(\"heladell/Finance_DeepSeek-R1-Distill-Llama-8B_QLoRA\", token = \"\")\n",
        "tokenizer.push_to_hub(\"heladell/Finance_DeepSeek-R1-Distill-Llama-8B_QLoRA\", token = \"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AEEcJ4qfC7Lp"
      },
      "source": [
        "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MKX_XKs_BNZR",
        "outputId": "2edfe013-d640-4ed9-c221-5bb14ab0c6ce"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "==((====))==  Unsloth 2025.1.8: Fast Llama patching. Transformers: 4.47.1.\n",
            "   \\\\   /|    GPU: NVIDIA A100-SXM4-40GB. Max memory: 39.557 GB. Platform: Linux.\n",
            "O^O/ \\_/ \\    Torch: 2.5.1+cu124. CUDA: 8.0. CUDA Toolkit: 12.4. Triton: 3.1.0\n",
            "\\        /    Bfloat16 = TRUE. FA [Xformers = 0.0.29. FA2 = False]\n",
            " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n",
            "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n",
            "<|im_start|>user\n",
            "how can you use technical analysis like MACD and RSI to predict the stock value<|im_end|>\n",
            "<|im_start|>assistant\n",
            "<think>\n",
            "Okay, so I want to learn how to use technical analysis tools like MACD and RSI to predict stock prices. I've heard about these indicators before, but I'm not exactly sure how they work or how to use them. Let me start by breaking down what each of these tools is and then figure out how they can be used together.\n",
            "\n",
            "First, MACD. I remember it stands for Moving Average Convergence Divergence. From what I recall, it's a momentum indicator that helps determine when a stock is in an uptrend or a downtrend. It uses two lines: the MACD line and the signal line. The MACD line is a moving average of the stock's price, and the signal line is another moving average of the MACD line. When they cross, it's supposed to signal a potential change in trend.\n",
            "\n",
            "RSI, on the other hand, is the Relative Strength Index. It measures the strength of a stock's price movement relative to its average. It's usually plotted on a scale from 0 to 100, where 0 indicates weakness and 100 indicates strength. I think it helps identify overbought or oversold conditions, which are points where a stock might be due for a price reversal.\n",
            "\n",
            "So, how do I use these together? Maybe I can look for when RSI is overbought (above 70) and the MACD is bearish (signal line above the MACD line) to signal a potential downtrend. Conversely, if RSI is oversold (below 30) and MACD is bullish (signal line below the MACD line), it might indicate an uptrend.\n",
            "\n",
            "I should also consider other indicators or signals, like volume or other oscillators like Stochastic RSI. Volume is important because it shows how strong the move is. If the volume is high when RSI is overbought, it might be a stronger signal.\n",
            "\n",
            "Another thing is timing. Sometimes, these indicators can give early signals, but they might not always be reliable. It's important to look for confirmation from other indicators or from on-chain metrics like order flow or market sentiment.\n",
            "\n",
            "I'm also wondering about the time frames. Should I use daily, weekly, or even intraday charts? I think higher time frames might give clearer signals, but lower time frames could provide more frequent updates. Maybe combining multiple time frames could help.\n",
            "\n",
            "I should also think about the context. Sometimes, the market is in a strong uptrend, so even if RSI is overbought\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "[\"<|im_start|>user\\nhow can you use technical analysis like MACD and RSI to predict the stock value<|im_end|>\\n<|im_start|>assistant\\n<think>\\nOkay, so I want to learn how to use technical analysis tools like MACD and RSI to predict stock prices. I've heard about these indicators before, but I'm not exactly sure how they work or how to use them. Let me start by breaking down what each of these tools is and then figure out how they can be used together.\\n\\nFirst, MACD. I remember it stands for Moving Average Convergence Divergence. From what I recall, it's a momentum indicator that helps determine when a stock is in an uptrend or a downtrend. It uses two lines: the MACD line and the signal line. The MACD line is a moving average of the stock's price, and the signal line is another moving average of the MACD line. When they cross, it's supposed to signal a potential change in trend.\\n\\nRSI, on the other hand, is the Relative Strength Index. It measures the strength of a stock's price movement relative to its average. It's usually plotted on a scale from 0 to 100, where 0 indicates weakness and 100 indicates strength. I think it helps identify overbought or oversold conditions, which are points where a stock might be due for a price reversal.\\n\\nSo, how do I use these together? Maybe I can look for when RSI is overbought (above 70) and the MACD is bearish (signal line above the MACD line) to signal a potential downtrend. Conversely, if RSI is oversold (below 30) and MACD is bullish (signal line below the MACD line), it might indicate an uptrend.\\n\\nI should also consider other indicators or signals, like volume or other oscillators like Stochastic RSI. Volume is important because it shows how strong the move is. If the volume is high when RSI is overbought, it might be a stronger signal.\\n\\nAnother thing is timing. Sometimes, these indicators can give early signals, but they might not always be reliable. It's important to look for confirmation from other indicators or from on-chain metrics like order flow or market sentiment.\\n\\nI'm also wondering about the time frames. Should I use daily, weekly, or even intraday charts? I think higher time frames might give clearer signals, but lower time frames could provide more frequent updates. Maybe combining multiple time frames could help.\\n\\nI should also think about the context. Sometimes, the market is in a strong uptrend, so even if RSI is overbought\"]"
            ]
          },
          "execution_count": 40,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "if True:\n",
        "    # from unsloth import FastLanguageModel\n",
        "    model, tokenizer = FastLanguageModel.from_pretrained(\n",
        "        model_name = \"Finance_DeepSeek-R1-Distill-Llama-8B_QLoRA\",\n",
        "        max_seq_length = max_seq_length,\n",
        "        dtype = dtype,\n",
        "        load_in_4bit = load_in_4bit,\n",
        "    )\n",
        "    FastLanguageModel.for_inference(model)\n",
        "\n",
        "\n",
        "# Load model for inference\n",
        "model = FastLanguageModel.for_inference(model)\n",
        "\n",
        "messages = [\n",
        "    {\"from\": \"human\", \"value\": \"how can you use technical analysis like MACD and RSI to predict the stock value\"},\n",
        "]\n",
        "inputs = tokenizer.apply_chat_template(\n",
        "    messages,\n",
        "    tokenize=True,\n",
        "    add_generation_prompt=True,\n",
        "    return_tensors=\"pt\",\n",
        ").to(\"cuda\")\n",
        "\n",
        "text_streamer = TextStreamer(tokenizer)\n",
        "outputs = model.generate(input_ids=inputs, streamer=text_streamer, max_new_tokens=512, use_cache=True)\n",
        "tokenizer.batch_decode(outputs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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            "bcf7179946b741f793f8dbef2b0fde0b",
            "fb71a9bd0cc7405f9bc4b28a791ea2c0",
            "4b83c896a35f4690a9c439fa15879b2e",
            "7726156541564a2dbdfdf2bd1ff345dd",
            "de3b66ba98814d849b34c764e91a8d29",
            "c6e1bd534bb94bb8ba05c48c4fba9663",
            "46e8c2ad3e8f49809a602d05a4e573c0",
            "d772afeed3874e7cb172598df99f16da",
            "f0974432dd20421e8753a43d05d7a233",
            "7e28915f723e4eff84c30a6930491b25",
            "8e4d99d7e7fb43d2bef6d6a4b139e252",
            "ef8d2f2b83d84202b4993a9d310084f0",
            "9c5a76890dc845649f5dd7f8b1eef47d",
            "fdc411504b424d9dad492c5155443baf"
          ]
        },
        "id": "1o5sZcSC7CEv",
        "outputId": "cc870e6b-e1be-4279-9d22-2ed73278c432"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Unsloth: Kaggle/Colab has limited disk space. We need to delete the downloaded\n",
            "model which will save 4-16GB of disk space, allowing you to save on Kaggle/Colab.\n",
            "Unsloth: Will remove a cached repo with size 6.0G\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unsloth: Merging 4bit and LoRA weights to 16bit...\n",
            "Unsloth: Will use up to 58.55 out of 83.48 RAM for saving.\n",
            "Unsloth: Saving model... This might take 5 minutes ...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 32/32 [00:00<00:00, 50.65it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unsloth: Saving tokenizer... Done.\n",
            "Done.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Unsloth: Converting llama model. Can use fast conversion = False.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "==((====))==  Unsloth: Conversion from QLoRA to GGUF information\n",
            "   \\\\   /|    [0] Installing llama.cpp might take 3 minutes.\n",
            "O^O/ \\_/ \\    [1] Converting HF to GGUF 16bits might take 3 minutes.\n",
            "\\        /    [2] Converting GGUF 16bits to ['q8_0'] might take 10 minutes each.\n",
            " \"-____-\"     In total, you will have to wait at least 16 minutes.\n",
            "\n",
            "Unsloth: Installing llama.cpp. This might take 3 minutes...\n",
            "Unsloth: CMAKE detected. Finalizing some steps for installation.\n",
            "Unsloth: [1] Converting model at Finance_DeepSeek-R1-Distill-Llama-8B_LoRA-Q8_0 into q8_0 GGUF format.\n",
            "The output location will be /content/Finance_DeepSeek-R1-Distill-Llama-8B_LoRA-Q8_0/unsloth.Q8_0.gguf\n",
            "This might take 3 minutes...\n",
            "INFO:hf-to-gguf:Loading model: Finance_DeepSeek-R1-Distill-Llama-8B_LoRA-Q8_0\n",
            "INFO:gguf.gguf_writer:gguf: This GGUF file is for Little Endian only\n",
            "INFO:hf-to-gguf:Exporting model...\n",
            "INFO:hf-to-gguf:rope_freqs.weight,           torch.float32 --> F32, shape = {64}\n",
            "INFO:hf-to-gguf:gguf: loading model weight map from 'model.safetensors.index.json'\n",
            "INFO:hf-to-gguf:gguf: loading model part 'model-00001-of-00004.safetensors'\n",
            "INFO:hf-to-gguf:token_embd.weight,           torch.bfloat16 --> Q8_0, shape = {4096, 128256}\n",
            "INFO:hf-to-gguf:blk.0.attn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.0.ffn_down.weight,       torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.0.ffn_gate.weight,       torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.0.ffn_up.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.0.ffn_norm.weight,       torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.0.attn_k.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.0.attn_output.weight,    torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.0.attn_q.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.0.attn_v.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.1.attn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.1.ffn_down.weight,       torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.1.ffn_gate.weight,       torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.1.ffn_up.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.1.ffn_norm.weight,       torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.1.attn_k.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.1.attn_output.weight,    torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.1.attn_q.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.1.attn_v.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.2.attn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.2.ffn_down.weight,       torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.2.ffn_gate.weight,       torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.2.ffn_up.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.2.ffn_norm.weight,       torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.2.attn_k.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.2.attn_output.weight,    torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.2.attn_q.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.2.attn_v.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.3.attn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.3.ffn_down.weight,       torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.3.ffn_gate.weight,       torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.3.ffn_up.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.3.ffn_norm.weight,       torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.3.attn_k.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.3.attn_output.weight,    torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.3.attn_q.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.3.attn_v.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.4.attn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.4.ffn_down.weight,       torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.4.ffn_gate.weight,       torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.4.ffn_up.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.4.ffn_norm.weight,       torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.4.attn_k.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.4.attn_output.weight,    torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.4.attn_q.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.4.attn_v.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.5.attn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.5.ffn_down.weight,       torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.5.ffn_gate.weight,       torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.5.ffn_up.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.5.ffn_norm.weight,       torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.5.attn_k.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.5.attn_output.weight,    torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.5.attn_q.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.5.attn_v.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.6.attn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.6.ffn_down.weight,       torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.6.ffn_gate.weight,       torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.6.ffn_up.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.6.ffn_norm.weight,       torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.6.attn_k.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.6.attn_output.weight,    torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.6.attn_q.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.6.attn_v.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.7.attn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.7.ffn_down.weight,       torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.7.ffn_gate.weight,       torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.7.ffn_up.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.7.ffn_norm.weight,       torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.7.attn_k.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.7.attn_output.weight,    torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.7.attn_q.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.7.attn_v.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.8.attn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.8.ffn_down.weight,       torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.8.ffn_gate.weight,       torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.8.ffn_up.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.8.ffn_norm.weight,       torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.8.attn_k.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.8.attn_output.weight,    torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.8.attn_q.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.8.attn_v.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:gguf: loading model part 'model-00002-of-00004.safetensors'\n",
            "INFO:hf-to-gguf:blk.10.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.10.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.10.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.10.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.10.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.10.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.10.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.10.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.10.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.11.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.11.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.11.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.11.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.11.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.11.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.11.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.11.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.11.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.12.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.12.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.12.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.12.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.12.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.12.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.12.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.12.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.12.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.13.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.13.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.13.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.13.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.13.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.13.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.13.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.13.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.13.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.14.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.14.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.14.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.14.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.14.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.14.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.14.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.14.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.14.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.15.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.15.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.15.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.15.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.15.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.15.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.15.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.15.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.15.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.16.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.16.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.16.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.16.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.16.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.16.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.16.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.16.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.16.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.17.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.17.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.17.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.17.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.17.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.17.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.17.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.17.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.17.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.18.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.18.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.18.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.18.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.18.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.18.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.18.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.18.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.18.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.19.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.19.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.19.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.19.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.19.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.19.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.19.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.19.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.19.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.20.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.20.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.20.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.20.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.20.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.9.attn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.9.ffn_down.weight,       torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.9.ffn_gate.weight,       torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.9.ffn_up.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.9.ffn_norm.weight,       torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.9.attn_k.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.9.attn_output.weight,    torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.9.attn_q.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.9.attn_v.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:gguf: loading model part 'model-00003-of-00004.safetensors'\n",
            "INFO:hf-to-gguf:blk.20.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.20.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.20.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.20.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.21.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.21.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.21.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.21.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.21.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.21.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.21.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.21.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.21.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.22.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.22.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.22.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.22.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.22.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.22.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.22.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.22.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.22.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.23.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.23.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.23.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.23.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.23.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.23.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.23.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.23.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.23.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.24.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.24.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.24.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.24.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.24.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.24.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.24.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.24.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.24.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.25.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.25.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.25.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.25.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.25.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.25.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.25.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.25.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.25.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.26.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.26.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.26.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.26.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.26.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.26.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.26.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.26.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.26.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.27.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.27.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.27.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.27.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.27.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.27.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.27.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.27.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.27.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.28.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.28.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.28.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.28.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.28.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.28.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.28.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.28.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.28.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.29.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.29.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.29.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.29.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.29.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.29.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.29.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.29.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.29.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.30.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.30.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.30.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.30.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.30.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.30.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.30.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.30.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.30.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.31.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.31.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.31.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.31.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.31.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.31.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:gguf: loading model part 'model-00004-of-00004.safetensors'\n",
            "INFO:hf-to-gguf:output.weight,               torch.bfloat16 --> Q8_0, shape = {4096, 128256}\n",
            "INFO:hf-to-gguf:blk.31.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.31.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.31.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:output_norm.weight,          torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:Set meta model\n",
            "INFO:hf-to-gguf:Set model parameters\n",
            "INFO:hf-to-gguf:gguf: context length = 131072\n",
            "INFO:hf-to-gguf:gguf: embedding length = 4096\n",
            "INFO:hf-to-gguf:gguf: feed forward length = 14336\n",
            "INFO:hf-to-gguf:gguf: head count = 32\n",
            "INFO:hf-to-gguf:gguf: key-value head count = 8\n",
            "INFO:hf-to-gguf:gguf: rope theta = 500000.0\n",
            "INFO:hf-to-gguf:gguf: rms norm epsilon = 1e-05\n",
            "INFO:hf-to-gguf:gguf: file type = 7\n",
            "INFO:hf-to-gguf:Set model tokenizer\n",
            "INFO:numexpr.utils:NumExpr defaulting to 12 threads.\n",
            "2025-02-04 12:35:18.260846: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
            "E0000 00:00:1738672518.286385   16910 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "E0000 00:00:1738672518.292279   16910 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "INFO:gguf.vocab:Adding 280147 merge(s).\n",
            "INFO:gguf.vocab:Setting special token type bos to 128000\n",
            "INFO:gguf.vocab:Setting special token type eos to 128001\n",
            "INFO:gguf.vocab:Setting special token type pad to 128004\n",
            "INFO:gguf.vocab:Setting add_bos_token to True\n",
            "INFO:gguf.vocab:Setting add_eos_token to False\n",
            "INFO:gguf.vocab:Setting chat_template to {% if 'role' in messages[0] %}{% for message in messages %}{% if message['role'] == 'user' %}{{'<|im_start|>user\n",
            "' + message['content'] + '<|im_end|>\n",
            "'}}{% elif message['role'] == 'assistant' %}{{'<|im_start|>assistant\n",
            "' + message['content'] + '<|im_end|>\n",
            "' }}{% else %}{{ '<|im_start|>system\n",
            "' + message['content'] + '<|im_end|>\n",
            "' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n",
            "' }}{% endif %}{% else %}{% for message in messages %}{% if message['from'] == 'human' %}{{'<|im_start|>user\n",
            "' + message['value'] + '<|im_end|>\n",
            "'}}{% elif message['from'] == 'gpt' %}{{'<|im_start|>assistant\n",
            "' + message['value'] + '<|im_end|>\n",
            "' }}{% else %}{{ '<|im_start|>system\n",
            "' + message['value'] + '<|im_end|>\n",
            "' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n",
            "' }}{% endif %}{% endif %}\n",
            "INFO:hf-to-gguf:Set model quantization version\n",
            "INFO:gguf.gguf_writer:Writing the following files:\n",
            "INFO:gguf.gguf_writer:/content/Finance_DeepSeek-R1-Distill-Llama-8B_LoRA-Q8_0/unsloth.Q8_0.gguf: n_tensors = 292, total_size = 8.5G\n",
            "Writing: 100%|██████████| 8.53G/8.53G [01:41<00:00, 84.0Mbyte/s]\n",
            "INFO:hf-to-gguf:Model successfully exported to /content/Finance_DeepSeek-R1-Distill-Llama-8B_LoRA-Q8_0/unsloth.Q8_0.gguf\n",
            "Unsloth: Conversion completed! Output location: /content/Finance_DeepSeek-R1-Distill-Llama-8B_LoRA-Q8_0/unsloth.Q8_0.gguf\n",
            "Unsloth: Merging 4bit and LoRA weights to 16bit...\n",
            "Unsloth: Will use up to 58.79 out of 83.48 RAM for saving.\n",
            "Unsloth: Saving model... This might take 5 minutes ...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 32/32 [00:00<00:00, 66.06it/s]\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Unsloth: Saving tokenizer... Done.\n",
            "Done.\n",
            "==((====))==  Unsloth: Conversion from QLoRA to GGUF information\n",
            "   \\\\   /|    [0] Installing llama.cpp might take 3 minutes.\n",
            "O^O/ \\_/ \\    [1] Converting HF to GGUF 16bits might take 3 minutes.\n",
            "\\        /    [2] Converting GGUF 16bits to ['q8_0'] might take 10 minutes each.\n",
            " \"-____-\"     In total, you will have to wait at least 16 minutes.\n",
            "\n",
            "Unsloth: Installing llama.cpp. This might take 3 minutes...\n",
            "Unsloth: [1] Converting model at heladell/Finance_DeepSeek-R1-Distill-Llama-8B_LoRA-Q8_0 into q8_0 GGUF format.\n",
            "The output location will be /content/heladell/Finance_DeepSeek-R1-Distill-Llama-8B_LoRA-Q8_0/unsloth.Q8_0.gguf\n",
            "This might take 3 minutes...\n",
            "INFO:hf-to-gguf:Loading model: Finance_DeepSeek-R1-Distill-Llama-8B_LoRA-Q8_0\n",
            "INFO:gguf.gguf_writer:gguf: This GGUF file is for Little Endian only\n",
            "INFO:hf-to-gguf:Exporting model...\n",
            "INFO:hf-to-gguf:rope_freqs.weight,           torch.float32 --> F32, shape = {64}\n",
            "INFO:hf-to-gguf:gguf: loading model weight map from 'model.safetensors.index.json'\n",
            "INFO:hf-to-gguf:gguf: loading model part 'model-00001-of-00004.safetensors'\n",
            "INFO:hf-to-gguf:token_embd.weight,           torch.bfloat16 --> Q8_0, shape = {4096, 128256}\n",
            "INFO:hf-to-gguf:blk.0.attn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.0.ffn_down.weight,       torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.0.ffn_gate.weight,       torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.0.ffn_up.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.0.ffn_norm.weight,       torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.0.attn_k.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.0.attn_output.weight,    torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.0.attn_q.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.0.attn_v.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.1.attn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.1.ffn_down.weight,       torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.1.ffn_gate.weight,       torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.1.ffn_up.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.1.ffn_norm.weight,       torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.1.attn_k.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.1.attn_output.weight,    torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.1.attn_q.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.1.attn_v.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.2.attn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.2.ffn_down.weight,       torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.2.ffn_gate.weight,       torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.2.ffn_up.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.2.ffn_norm.weight,       torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.2.attn_k.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.2.attn_output.weight,    torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.2.attn_q.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.2.attn_v.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.3.attn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.3.ffn_down.weight,       torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.3.ffn_gate.weight,       torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.3.ffn_up.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.3.ffn_norm.weight,       torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.3.attn_k.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.3.attn_output.weight,    torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.3.attn_q.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.3.attn_v.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.4.attn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.4.ffn_down.weight,       torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.4.ffn_gate.weight,       torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.4.ffn_up.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.4.ffn_norm.weight,       torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.4.attn_k.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.4.attn_output.weight,    torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.4.attn_q.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.4.attn_v.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.5.attn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.5.ffn_down.weight,       torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.5.ffn_gate.weight,       torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.5.ffn_up.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.5.ffn_norm.weight,       torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.5.attn_k.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.5.attn_output.weight,    torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.5.attn_q.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.5.attn_v.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.6.attn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.6.ffn_down.weight,       torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.6.ffn_gate.weight,       torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.6.ffn_up.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.6.ffn_norm.weight,       torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.6.attn_k.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.6.attn_output.weight,    torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.6.attn_q.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.6.attn_v.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.7.attn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.7.ffn_down.weight,       torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.7.ffn_gate.weight,       torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.7.ffn_up.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.7.ffn_norm.weight,       torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.7.attn_k.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.7.attn_output.weight,    torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.7.attn_q.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.7.attn_v.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.8.attn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.8.ffn_down.weight,       torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.8.ffn_gate.weight,       torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.8.ffn_up.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.8.ffn_norm.weight,       torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.8.attn_k.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.8.attn_output.weight,    torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.8.attn_q.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.8.attn_v.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:gguf: loading model part 'model-00002-of-00004.safetensors'\n",
            "INFO:hf-to-gguf:blk.10.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.10.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.10.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.10.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.10.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.10.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.10.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.10.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.10.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.11.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.11.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.11.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.11.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.11.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.11.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.11.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.11.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.11.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.12.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.12.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.12.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.12.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.12.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.12.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.12.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.12.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.12.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.13.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.13.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.13.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.13.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.13.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.13.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.13.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.13.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.13.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.14.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.14.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.14.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.14.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.14.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.14.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.14.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.14.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.14.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.15.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.15.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.15.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.15.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.15.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.15.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.15.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.15.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.15.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.16.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.16.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.16.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.16.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.16.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.16.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.16.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.16.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.16.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.17.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.17.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.17.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.17.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.17.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.17.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.17.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.17.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.17.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.18.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.18.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.18.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.18.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.18.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.18.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.18.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.18.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.18.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.19.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.19.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.19.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.19.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.19.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.19.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.19.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.19.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.19.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.20.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.20.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.20.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.20.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.20.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.9.attn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.9.ffn_down.weight,       torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.9.ffn_gate.weight,       torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.9.ffn_up.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.9.ffn_norm.weight,       torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.9.attn_k.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.9.attn_output.weight,    torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.9.attn_q.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.9.attn_v.weight,         torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:gguf: loading model part 'model-00003-of-00004.safetensors'\n",
            "INFO:hf-to-gguf:blk.20.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.20.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.20.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.20.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.21.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.21.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.21.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.21.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.21.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.21.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.21.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.21.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.21.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.22.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.22.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.22.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.22.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.22.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.22.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.22.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.22.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.22.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.23.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.23.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.23.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.23.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.23.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.23.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.23.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.23.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.23.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.24.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.24.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.24.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.24.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.24.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.24.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.24.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.24.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.24.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.25.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.25.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.25.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.25.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.25.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.25.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.25.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.25.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.25.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.26.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.26.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.26.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.26.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.26.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.26.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.26.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.26.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.26.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.27.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.27.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.27.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.27.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.27.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.27.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.27.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.27.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.27.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.28.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.28.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.28.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.28.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.28.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.28.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.28.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.28.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.28.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.29.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.29.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.29.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.29.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.29.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.29.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.29.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.29.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.29.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.30.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.30.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.30.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.30.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.30.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.30.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.30.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.30.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.30.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.31.ffn_gate.weight,      torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.31.ffn_up.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 14336}\n",
            "INFO:hf-to-gguf:blk.31.attn_k.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:blk.31.attn_output.weight,   torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.31.attn_q.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 4096}\n",
            "INFO:hf-to-gguf:blk.31.attn_v.weight,        torch.bfloat16 --> Q8_0, shape = {4096, 1024}\n",
            "INFO:hf-to-gguf:gguf: loading model part 'model-00004-of-00004.safetensors'\n",
            "INFO:hf-to-gguf:output.weight,               torch.bfloat16 --> Q8_0, shape = {4096, 128256}\n",
            "INFO:hf-to-gguf:blk.31.attn_norm.weight,     torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:blk.31.ffn_down.weight,      torch.bfloat16 --> Q8_0, shape = {14336, 4096}\n",
            "INFO:hf-to-gguf:blk.31.ffn_norm.weight,      torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:output_norm.weight,          torch.bfloat16 --> F32, shape = {4096}\n",
            "INFO:hf-to-gguf:Set meta model\n",
            "INFO:hf-to-gguf:Set model parameters\n",
            "INFO:hf-to-gguf:gguf: context length = 131072\n",
            "INFO:hf-to-gguf:gguf: embedding length = 4096\n",
            "INFO:hf-to-gguf:gguf: feed forward length = 14336\n",
            "INFO:hf-to-gguf:gguf: head count = 32\n",
            "INFO:hf-to-gguf:gguf: key-value head count = 8\n",
            "INFO:hf-to-gguf:gguf: rope theta = 500000.0\n",
            "INFO:hf-to-gguf:gguf: rms norm epsilon = 1e-05\n",
            "INFO:hf-to-gguf:gguf: file type = 7\n",
            "INFO:hf-to-gguf:Set model tokenizer\n",
            "INFO:numexpr.utils:NumExpr defaulting to 12 threads.\n",
            "2025-02-04 12:38:14.805461: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
            "E0000 00:00:1738672694.830844   17681 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "E0000 00:00:1738672694.838017   17681 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "INFO:gguf.vocab:Adding 280147 merge(s).\n",
            "INFO:gguf.vocab:Setting special token type bos to 128000\n",
            "INFO:gguf.vocab:Setting special token type eos to 128001\n",
            "INFO:gguf.vocab:Setting special token type pad to 128004\n",
            "INFO:gguf.vocab:Setting add_bos_token to True\n",
            "INFO:gguf.vocab:Setting add_eos_token to False\n",
            "INFO:gguf.vocab:Setting chat_template to {% if 'role' in messages[0] %}{% for message in messages %}{% if message['role'] == 'user' %}{{'<|im_start|>user\n",
            "' + message['content'] + '<|im_end|>\n",
            "'}}{% elif message['role'] == 'assistant' %}{{'<|im_start|>assistant\n",
            "' + message['content'] + '<|im_end|>\n",
            "' }}{% else %}{{ '<|im_start|>system\n",
            "' + message['content'] + '<|im_end|>\n",
            "' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n",
            "' }}{% endif %}{% else %}{% for message in messages %}{% if message['from'] == 'human' %}{{'<|im_start|>user\n",
            "' + message['value'] + '<|im_end|>\n",
            "'}}{% elif message['from'] == 'gpt' %}{{'<|im_start|>assistant\n",
            "' + message['value'] + '<|im_end|>\n",
            "' }}{% else %}{{ '<|im_start|>system\n",
            "' + message['value'] + '<|im_end|>\n",
            "' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n",
            "' }}{% endif %}{% endif %}\n",
            "INFO:hf-to-gguf:Set model quantization version\n",
            "INFO:gguf.gguf_writer:Writing the following files:\n",
            "INFO:gguf.gguf_writer:/content/heladell/Finance_DeepSeek-R1-Distill-Llama-8B_LoRA-Q8_0/unsloth.Q8_0.gguf: n_tensors = 292, total_size = 8.5G\n",
            "Writing: 100%|██████████| 8.53G/8.53G [01:41<00:00, 84.4Mbyte/s]\n",
            "INFO:hf-to-gguf:Model successfully exported to /content/heladell/Finance_DeepSeek-R1-Distill-Llama-8B_LoRA-Q8_0/unsloth.Q8_0.gguf\n",
            "Unsloth: Conversion completed! Output location: /content/heladell/Finance_DeepSeek-R1-Distill-Llama-8B_LoRA-Q8_0/unsloth.Q8_0.gguf\n",
            "Unsloth: Uploading GGUF to Huggingface Hub...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "40fc8615d07e41a1b029a62c3c550e74",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "  0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7726156541564a2dbdfdf2bd1ff345dd",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "unsloth.Q8_0.gguf:   0%|          | 0.00/8.54G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No files have been modified since last commit. Skipping to prevent empty commit.\n",
            "WARNING:huggingface_hub.hf_api:No files have been modified since last commit. Skipping to prevent empty commit.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Saved GGUF to https://huggingface.co/heladell/Finance_DeepSeek-R1-Distill-Llama-8B_LoRA-Q8_0\n"
          ]
        }
      ],
      "source": [
        "model.save_pretrained_gguf(\"Finance_DeepSeek-R1-Distill-Llama-8B_LoRA-Q8_0\", tokenizer,)\n",
        "model.push_to_hub_gguf(\"heladell/Finance_DeepSeek-R1-Distill-Llama-8B_LoRA-Q8_0\", tokenizer, token = \"\")"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "A100",
      "machine_shape": "hm",
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